Efficient Prediction of Vitamin B Deficiencies via Machine-Learning Using Routine Blood Test Results in Patients With Intense Psychiatric Episode

Tamune, Hidetaka; Ukita, Jumpei; Hamamoto, Yoshiyuki; Tanaka, Hiroko; Narushima, Kenji; Yamamoto, Naoki

doi:10.3389/fpsyt.2019.01029

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…The use of laboratory tests and machine learning to search for new results has been extensively explored in recent years [ 8 , 14 , 20 , 29 – 33 ]. In particular, we draw attention to the work of Park [ 34 ], who performed the prediction of several diseases using laboratory tests, but not including DM.…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening

Cardozo

Pintarelli

Andreis

et al. 2022

BioMed Research International

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Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing.

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Section: Introductionmentioning

confidence: 99%

Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening

Cardozo

Pintarelli

Andreis

et al. 2022

BioMed Research International

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Challenges in data acquisition for suicide attempt recurrence research

Tamune

2024

The American Journal of Emergency Medicine

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A decision tree prediction model for a short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions: A secondary analysis of a multicenter and prospective observational study (Phase-R)

Kurisu

Inada

Maeda³

et al. 2021

Pall Supp Care

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Objective There is no widely used prognostic model for delirium in patients with advanced cancer. The present study aimed to develop a decision tree prediction model for a short-term outcome. Method This is a secondary analysis of a multicenter and prospective observational study conducted at 9 psycho-oncology consultation services and 14 inpatient palliative care units in Japan. We used records of patients with advanced cancer receiving pharmacological interventions with a baseline Delirium Rating Scale Revised-98 (DRS-R98) severity score of ≥10. A DRS-R98 severity score of <10 on day 3 was defined as the study outcome. The dataset was randomly split into the training and test dataset. A decision tree model was developed using the training dataset and potential predictors. The area under the curve (AUC) of the receiver operating characteristic curve was measured both in 5-fold cross-validation and in the independent test dataset. Finally, the model was visualized using the whole dataset. Results Altogether, 668 records were included, of which 141 had a DRS-R98 severity score of <10 on day 3. The model achieved an average AUC of 0.698 in 5-fold cross-validation and 0.718 (95% confidence interval, 0.627–0.810) in the test dataset. The baseline DRS-R98 severity score (cutoff of 15), hypoxia, and dehydration were the important predictors, in this order. Significance of results We developed an easy-to-use prediction model for the short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions. The baseline severity of delirium and precipitating factors of delirium were important for prediction.

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Efficient Prediction of Vitamin B Deficiencies via Machine-Learning Using Routine Blood Test Results in Patients With Intense Psychiatric Episode

Cited by 8 publications

References 26 publications

Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening

Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening

Challenges in data acquisition for suicide attempt recurrence research

A decision tree prediction model for a short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions: A secondary analysis of a multicenter and prospective observational study (Phase-R)

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